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Multitask diffusion affine projection sign algorithm and its sparse variant for distributed estimation
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DOI：10.1016/j.sigpro.2020.107561.png)
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Distributed adaptation over multitask networks has attracted particular attention due to its enhanced modeling capacity compared to that over conventional single-task networks. Most of the existing works derive their multitask adaptive algorithms using mean-square error (MSE) or least squares (LS) criterion, leading to multitask LMS- or LS-type algorithms. These algorithms, however, may suffer from deteriorated convergence rate or even divergence in impulsive noise environments. In order to address this problem, we propose a robust diffusion affine projection sign algorithm for multitask parameter estimation. The algorithm is derived by using the method of data reusing and minimizing the weighted sum of the l(1)-norms of some intermediate error vectors plus a similarity term subject to constraints on intermediate weight vectors at each agent. The multitask similarity relationship is characterized by the distance regularization among weight vectors. Furthermore, a variant of this algorithm, which is obtained by further regularizing the cost function by the 10-norm of the intermediate weight vector at each agent, is presented to promote convergence rate for jointly sparse parameter vectors estimation. (C) 2020 Elsevier B.V. All rights reserved.
Keyword:
Multitask network
Affine projection
Diffusion strategy
l(1)-norm optimization
Impulsive noise
Jointly sparse estimation
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